scholarly journals Wood Defect Detection Based on Depth Extreme Learning Machine

2020 ◽  
Vol 10 (21) ◽  
pp. 7488
Author(s):  
Yutu Yang ◽  
Xiaolin Zhou ◽  
Ying Liu ◽  
Zhongkang Hu ◽  
Fenglong Ding
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Defect Detection ◽  
Clustering Algorithm ◽  
Classification Performance ◽  
Image Features ◽  
Test Time ◽  
Machine Model ◽  
Feature Extraction Method ◽  
Learning Machine

The deep learning feature extraction method and extreme learning machine (ELM) classification method are combined to establish a depth extreme learning machine model for wood image defect detection. The convolution neural network (CNN) algorithm alone tends to provide inaccurate defect locations, incomplete defect contour and boundary information, and inaccurate recognition of defect types. The nonsubsampled shearlet transform (NSST) is used here to preprocess the wood images, which reduces the complexity and computation of the image processing. CNN is then applied to manage the deep algorithm design of the wood images. The simple linear iterative clustering algorithm is used to improve the initial model; the obtained image features are used as ELM classification inputs. ELM has faster training speed and stronger generalization ability than other similar neural networks, but the random selection of input weights and thresholds degrades the classification accuracy. A genetic algorithm is used here to optimize the initial parameters of the ELM to stabilize the network classification performance. The depth extreme learning machine can extract high-level abstract information from the data, does not require iterative adjustment of the network weights, has high calculation efficiency, and allows CNN to effectively extract the wood defect contour. The distributed input data feature is automatically expressed in layer form by deep learning pre-training. The wood defect recognition accuracy reached 96.72% in a test time of only 187 ms.

scholarly journals Extended Hierarchical Extreme Learning Machine with Multilayer Perceptron

2017 ◽  
Vol 10 (2) ◽  
pp. 196-204
Author(s):  
Khanittha Phumrattanaprapin ◽  
Punyaphol Horata
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Multilayer Perceptron ◽  
High Performance ◽  
Single Layer ◽  
Classification Performance ◽  
Classification Problems ◽  
Hierarchical Learning ◽  
Deep Architecture ◽  
Learning Machine

The Deep Learning approach provides a high performance of classification, especially when invoking image classification problems. However, a shortcoming of the traditional Deep Learning method is the large time scale of training. The hierarchical extreme learning machine (H-ELM) framework was based on the hierarchical learning architecture of multilayer perceptron to address the problem. H-ELM is composed of two parts; the first entails unsupervised multilayer encoding, and the second is the supervised feature classification. H-ELM can give a higher accuracy rate than the traditional ELM. However, there still remains room to enhance its classification performance. This paper therefore proposes a new method termed the extending hierarchical extreme learning machine (EH-ELM), which extends the number of layers in the supervised portion of the H-ELM from a single layer to multiple layers. To evaluate the performance of the EH-ELM, the various classification datasets were studied and compared with the H-ELM and the multilayer ELM, as well as various state-of-the-art such deep architecture methods. The experimental results show that the EH-ELM improved the accuracy rates over most other methods.

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

scholarly journals Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Pak Kin Wong ◽  
Hang Cheong Wong ◽  
Chi Man Vong ◽  
Tong Meng Iong ◽  
Ka In Wong ◽  
...  
Keyword(s):  
Fault Tolerance ◽  
Extreme Learning Machine ◽  
External Disturbance ◽  
Feedback Signal ◽  
Ratio Model ◽  
Control Performance ◽  
Machine Model ◽  
Target Values ◽  
Lambda Sensor ◽  
Learning Machine

Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (ELM), to build a backup air-ratio model. With the prediction from the model, a limited air-ratio control performance can be maintained even when the lambda sensor does not work. Such strategy is realized as fault tolerance control. In order to verify the effectiveness of the proposed fault tolerance air-ratio control strategy, a model predictive control scheme is constructed based on the kernel ELM backup air-ratio model and implemented on a real engine. Experimental results show that the proposed controller can regulate the air-ratio to specific target values within a satisfactory tolerance under external disturbance and the absence of air-ratio feedback signal from the lambda sensor. This implies that the proposed fault tolerance air-ratio control is a promising scheme to maintain air-ratio control performance when the lambda sensor is under failure or warming up.

Preference-driven yield-and-quality optimization for high-sulfur gas sweetening process by extreme learning machine model

2018 ◽  
Vol 22 (S3) ◽  
pp. 6371-6381
Author(s):  
Jianfeng Shang ◽  
Xiaohua Gu ◽  
Liping Yang ◽  
Haihong Tang ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Machine Model ◽  
Quality Optimization ◽  
Yield And Quality ◽  
Gas Sweetening ◽  
Learning Machine

scholarly journals Convolutional Extreme Learning Machines: A Systematic Review

2021 ◽  
Author(s):  
Iago Richard Rodrigues ◽  
Sebastião Rogério ◽  
Judith Kelner ◽  
Djamel Sadok ◽  
Patricia Takako Endo
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Research Area ◽  
New Paradigm ◽  
Learning Machines ◽  
Starting Point ◽  
Benchmark Datasets ◽  
Good Starting Point ◽  
Learning Machine

Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

Sign in / Sign up

Export Citation Format

Share Document